Schizophrenia (SZ) is an often-devastating neuropsychiatric illness. Genetic factors have been strongly implicated. The genetic contribution to SZ is likely to be complex and to extend beyond sequence variation. DNA methylation studies represent a promising complement to analyses of DNA sequence. As methylation is related to gene expression, knowledge of methylation levels of, e.g., regulatory elements may contribute functional information. Furthermore, as DNA methylation changes over time, methylation studies can provide insight into phenomena such as age of onset and the episodic nature of SZ. In addition, the dynamic nature of methylation and its response to environmental exposures suggests that methylation may explain gene- environment interactions. In a recent study, including 1,500 SZ case-control samples, all CpGs in the methylome was investigated using the enrichment based MBD-seq approach. This approach employs a methyl-CpG binding domain protein with high affinity for methylated CpGs in combination with next-generation sequencing to investigate the methylation profile across all CpGs in the genome. The data was used to conduct a methylome-wide association study (MWAS) covering ~27 million CpGs in the autosomal human reference genome. Analysis of SZ case-control status resulted in significant CpGs of promising biological relevance such as hypoxia and immune response. The results where successfully replicated in an independent study-sample using highly quantitative bisulfite pyrosequencing. The current proposal aims to conduct new analysis of the methylome data. Phenotype information about birth complications (e.g., hypoxia and infection), and use of prescribed drugs, such as antipsychotics, as well as genotype information from GWAS SNP genotyping, exome-sequencing and exome-chips are available from the study sample, which will be used in the new analysis. The human genome consists of a large number of CpGs that are created/destroyed by SNPs. Using the MBD-seq approach the methylation status for these CpGs are already available. However, the analyses for these sites require a different approach than CpGs in general. By integrating methylation and SNP information the methylation signal in these sites can be investigated conditional on the sequence and the potential effect on methylation caused by SZ can be determined. Furthermore, even if a SNP itself does not change the CpG the SNP may be correlated with the methylation signal, a methylation quantitative trait locus (meQTL). Whether, the location and distribution of these meQTLs are associated with SZ status remains to be investigated. Another type of data integration involves datasets obtained from different study samples. If multiple data types, such as results from GWAS, MWAS and mRNA expression analysis point towards susceptibility in specific loci, these loci are more likely to be true findings than if they were detected by a single data type.